Overview

Dataset statistics

Number of variables18
Number of observations1511
Missing cells3045
Missing cells (%)11.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.6 KiB
Average record size in memory144.1 B

Variable types

Text8
Categorical5
Numeric5

Alerts

userId_x has 405 (26.8%) missing valuesMissing
proficiency has 26 (1.7%) missing valuesMissing
VerificationStatus has 26 (1.7%) missing valuesMissing
projectName has 400 (26.5%) missing valuesMissing
ProjectDescription has 400 (26.5%) missing valuesMissing
certificateName has 405 (26.8%) missing valuesMissing
CertificateDescription has 405 (26.8%) missing valuesMissing
issuingAuthority has 405 (26.8%) missing valuesMissing
CurentStatus has 26 (1.7%) missing valuesMissing
comments has 521 (34.5%) missing valuesMissing
skillName has 26 (1.7%) missing valuesMissing
hackerRankScore has 38 (2.5%) zerosZeros
validityPeriodMonths has 405 (26.8%) zerosZeros
rating has 521 (34.5%) zerosZeros
total_project_days has 400 (26.5%) zerosZeros
chanceToApprove has 405 (26.8%) zerosZeros

Reproduction

Analysis started2024-04-13 17:25:54.489957
Analysis finished2024-04-13 17:25:59.919899
Duration5.43 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

userId_x
Text

MISSING 

Distinct354
Distinct (%)32.0%
Missing405
Missing (%)26.8%
Memory size11.9 KiB
2024-04-13T22:56:00.111753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters26544
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)7.0%

Sample

1st row66129473addf941b98f24c4a
2nd row66129472addf941b98f24c39
3rd row66129484addf941b98f24d52
4th row6612946caddf941b98f24be3
5th row6612947daddf941b98f24cf0
ValueCountFrequency (%)
66129487addf941b98f24d7c 12
 
1.1%
6612946caddf941b98f24be3 12
 
1.1%
66129489addf941b98f24daa 10
 
0.9%
66129484addf941b98f24d4e 8
 
0.7%
6612947eaddf941b98f24cff 8
 
0.7%
66129477addf941b98f24c8a 8
 
0.7%
66129481addf941b98f24d23 8
 
0.7%
6612947eaddf941b98f24cfa 8
 
0.7%
66129486addf941b98f24d78 8
 
0.7%
66129470addf941b98f24c17 8
 
0.7%
Other values (344) 1016
91.9%
2024-04-13T22:56:00.498574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 3543
13.3%
9 3493
13.2%
d 2821
10.6%
6 2586
9.7%
1 2411
9.1%
f 2408
9.1%
2 2385
9.0%
8 1673
6.3%
b 1453
5.5%
a 1301
 
4.9%
Other values (6) 2470
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 3543
13.3%
9 3493
13.2%
d 2821
10.6%
6 2586
9.7%
1 2411
9.1%
f 2408
9.1%
2 2385
9.0%
8 1673
6.3%
b 1453
5.5%
a 1301
 
4.9%
Other values (6) 2470
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 3543
13.3%
9 3493
13.2%
d 2821
10.6%
6 2586
9.7%
1 2411
9.1%
f 2408
9.1%
2 2385
9.0%
8 1673
6.3%
b 1453
5.5%
a 1301
 
4.9%
Other values (6) 2470
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 3543
13.3%
9 3493
13.2%
d 2821
10.6%
6 2586
9.7%
1 2411
9.1%
f 2408
9.1%
2 2385
9.0%
8 1673
6.3%
b 1453
5.5%
a 1301
 
4.9%
Other values (6) 2470
9.3%

proficiency
Categorical

MISSING 

Distinct3
Distinct (%)0.2%
Missing26
Missing (%)1.7%
Memory size11.9 KiB
advanced
519 
intermediate
493 
beginner
473 

Length

Max length12
Median length8
Mean length9.3279461
Min length8

Characters and Unicode

Total characters13852
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowintermediate
2nd rowadvanced
3rd rowadvanced
4th rowadvanced
5th rowadvanced

Common Values

ValueCountFrequency (%)
advanced 519
34.3%
intermediate 493
32.6%
beginner 473
31.3%
(Missing) 26
 
1.7%

Length

2024-04-13T22:56:00.689505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T22:56:00.828470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
advanced 519
34.9%
intermediate 493
33.2%
beginner 473
31.9%

Most occurring characters

ValueCountFrequency (%)
e 2944
21.3%
n 1958
14.1%
a 1531
11.1%
d 1531
11.1%
i 1459
10.5%
t 986
 
7.1%
r 966
 
7.0%
v 519
 
3.7%
c 519
 
3.7%
m 493
 
3.6%
Other values (2) 946
 
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2944
21.3%
n 1958
14.1%
a 1531
11.1%
d 1531
11.1%
i 1459
10.5%
t 986
 
7.1%
r 966
 
7.0%
v 519
 
3.7%
c 519
 
3.7%
m 493
 
3.6%
Other values (2) 946
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2944
21.3%
n 1958
14.1%
a 1531
11.1%
d 1531
11.1%
i 1459
10.5%
t 986
 
7.1%
r 966
 
7.0%
v 519
 
3.7%
c 519
 
3.7%
m 493
 
3.6%
Other values (2) 946
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2944
21.3%
n 1958
14.1%
a 1531
11.1%
d 1531
11.1%
i 1459
10.5%
t 986
 
7.1%
r 966
 
7.0%
v 519
 
3.7%
c 519
 
3.7%
m 493
 
3.6%
Other values (2) 946
 
6.8%

VerificationStatus
Categorical

MISSING 

Distinct2
Distinct (%)0.1%
Missing26
Missing (%)1.7%
Memory size11.9 KiB
Not Verified
770 
Verified
715 

Length

Max length12
Median length12
Mean length10.074074
Min length8

Characters and Unicode

Total characters14960
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowVerified
3rd rowVerified
4th rowVerified
5th rowVerified

Common Values

ValueCountFrequency (%)
Not Verified 770
51.0%
Verified 715
47.3%
(Missing) 26
 
1.7%

Length

2024-04-13T22:56:00.984881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T22:56:01.126203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
verified 1485
65.9%
not 770
34.1%

Most occurring characters

ValueCountFrequency (%)
e 2970
19.9%
i 2970
19.9%
V 1485
9.9%
r 1485
9.9%
f 1485
9.9%
d 1485
9.9%
N 770
 
5.1%
o 770
 
5.1%
t 770
 
5.1%
770
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14960
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2970
19.9%
i 2970
19.9%
V 1485
9.9%
r 1485
9.9%
f 1485
9.9%
d 1485
9.9%
N 770
 
5.1%
o 770
 
5.1%
t 770
 
5.1%
770
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14960
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2970
19.9%
i 2970
19.9%
V 1485
9.9%
r 1485
9.9%
f 1485
9.9%
d 1485
9.9%
N 770
 
5.1%
o 770
 
5.1%
t 770
 
5.1%
770
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14960
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2970
19.9%
i 2970
19.9%
V 1485
9.9%
r 1485
9.9%
f 1485
9.9%
d 1485
9.9%
N 770
 
5.1%
o 770
 
5.1%
t 770
 
5.1%
770
 
5.1%

hackerRankScore
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.034414
Minimum0
Maximum100
Zeros38
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2024-04-13T22:56:01.323313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122
median48
Q376
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)54

Descriptive statistics

Standard deviation30.201445
Coefficient of variation (CV)0.61592344
Kurtosis-1.2487206
Mean49.034414
Median Absolute Deviation (MAD)27
Skewness0.020494467
Sum74091
Variance912.12729
MonotonicityNot monotonic
2024-04-13T22:56:01.553910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
2.5%
40 24
 
1.6%
99 23
 
1.5%
3 22
 
1.5%
7 22
 
1.5%
86 21
 
1.4%
60 21
 
1.4%
45 20
 
1.3%
31 20
 
1.3%
25 20
 
1.3%
Other values (91) 1280
84.7%
ValueCountFrequency (%)
0 38
2.5%
1 17
1.1%
2 12
 
0.8%
3 22
1.5%
4 13
 
0.9%
5 14
 
0.9%
6 12
 
0.8%
7 22
1.5%
8 13
 
0.9%
9 19
1.3%
ValueCountFrequency (%)
100 16
1.1%
99 23
1.5%
98 10
0.7%
97 15
1.0%
96 17
1.1%
95 17
1.1%
94 14
0.9%
93 14
0.9%
92 14
0.9%
91 19
1.3%

designation
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Principal Architect
278 
Consultant
268 
Software Engineer
261 
Architect
243 
Sr. Software Engineer
242 

Length

Max length21
Median length17
Mean length15.335539
Min length9

Characters and Unicode

Total characters23172
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArchitect
2nd rowArchitect
3rd rowPrincipal Architect
4th rowPrincipal Architect
5th rowPrincipal Architect

Common Values

ValueCountFrequency (%)
Principal Architect 278
18.4%
Consultant 268
17.7%
Software Engineer 261
17.3%
Architect 243
16.1%
Sr. Software Engineer 242
16.0%
Solution Enabler 219
14.5%

Length

2024-04-13T22:56:01.765502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T22:56:01.944259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
architect 521
18.9%
software 503
18.3%
engineer 503
18.3%
principal 278
10.1%
consultant 268
9.7%
sr 242
8.8%
solution 219
8.0%
enabler 219
8.0%

Most occurring characters

ValueCountFrequency (%)
t 2300
 
9.9%
r 2266
 
9.8%
n 2258
 
9.7%
e 2249
 
9.7%
i 1799
 
7.8%
c 1320
 
5.7%
a 1268
 
5.5%
1242
 
5.4%
o 1209
 
5.2%
l 984
 
4.2%
Other values (14) 6277
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2300
 
9.9%
r 2266
 
9.8%
n 2258
 
9.7%
e 2249
 
9.7%
i 1799
 
7.8%
c 1320
 
5.7%
a 1268
 
5.5%
1242
 
5.4%
o 1209
 
5.2%
l 984
 
4.2%
Other values (14) 6277
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2300
 
9.9%
r 2266
 
9.8%
n 2258
 
9.7%
e 2249
 
9.7%
i 1799
 
7.8%
c 1320
 
5.7%
a 1268
 
5.5%
1242
 
5.4%
o 1209
 
5.2%
l 984
 
4.2%
Other values (14) 6277
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2300
 
9.9%
r 2266
 
9.8%
n 2258
 
9.7%
e 2249
 
9.7%
i 1799
 
7.8%
c 1320
 
5.7%
a 1268
 
5.5%
1242
 
5.4%
o 1209
 
5.2%
l 984
 
4.2%
Other values (14) 6277
27.1%

projectName
Text

MISSING 

Distinct591
Distinct (%)53.2%
Missing400
Missing (%)26.5%
Memory size11.9 KiB
2024-04-13T22:56:02.304646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length28
Mean length16.60486
Min length7

Characters and Unicode

Total characters18448
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique263 ?
Unique (%)23.7%

Sample

1st rowHicks-Rodriguez
2nd rowKeller PLC
3rd rowSchultz, Jacobs and Warner
4th rowCampos and Sons
5th rowCline, Campbell and Gibson
ValueCountFrequency (%)
and 427
 
16.2%
group 74
 
2.8%
plc 72
 
2.7%
llc 65
 
2.5%
sons 63
 
2.4%
ltd 54
 
2.0%
inc 46
 
1.7%
smith 41
 
1.6%
thomas 27
 
1.0%
brown 25
 
0.9%
Other values (624) 1746
66.1%
2024-04-13T22:56:03.027354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1702
 
9.2%
a 1540
 
8.3%
1529
 
8.3%
o 1249
 
6.8%
e 1245
 
6.7%
r 1122
 
6.1%
l 871
 
4.7%
s 857
 
4.6%
d 784
 
4.2%
i 742
 
4.0%
Other values (43) 6807
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1702
 
9.2%
a 1540
 
8.3%
1529
 
8.3%
o 1249
 
6.8%
e 1245
 
6.7%
r 1122
 
6.1%
l 871
 
4.7%
s 857
 
4.6%
d 784
 
4.2%
i 742
 
4.0%
Other values (43) 6807
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1702
 
9.2%
a 1540
 
8.3%
1529
 
8.3%
o 1249
 
6.8%
e 1245
 
6.7%
r 1122
 
6.1%
l 871
 
4.7%
s 857
 
4.6%
d 784
 
4.2%
i 742
 
4.0%
Other values (43) 6807
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1702
 
9.2%
a 1540
 
8.3%
1529
 
8.3%
o 1249
 
6.8%
e 1245
 
6.7%
r 1122
 
6.1%
l 871
 
4.7%
s 857
 
4.6%
d 784
 
4.2%
i 742
 
4.0%
Other values (43) 6807
36.9%

ProjectDescription
Text

MISSING 

Distinct600
Distinct (%)54.0%
Missing400
Missing (%)26.5%
Memory size11.9 KiB
2024-04-13T22:56:03.431339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length201
Median length167
Mean length148.86229
Min length18

Characters and Unicode

Total characters165386
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)24.4%

Sample

1st rowName history consumer allow expert. Because enjoy establish perform recognize teacher. Social letter food over perhaps appear. Two walk least generation small admit. Thought different else week here.
2nd rowMachine network dream here ask detail single. Paper improve third laugh entire cause despite. He study others. Throughout executive old travel. Per health news. Put population us receive.
3rd rowRelationship employee wife. Design for hour office factor eight hear. Number tree school church capital. Mission daughter student may form summer. Consider impact heart.
4th rowInformation begin article true. Base body clearly hotel artist. Finish general different director. Public strong along business this former.
5th rowWhere worker drug investment those. Forward then box ask clear. Mean type model. Region minute through financial. Source election mission mind style painting senior.
ValueCountFrequency (%)
view 54
 
0.2%
also 53
 
0.2%
spend 50
 
0.2%
head 50
 
0.2%
mission 49
 
0.2%
much 49
 
0.2%
cause 48
 
0.2%
soldier 46
 
0.2%
machine 46
 
0.2%
me 46
 
0.2%
Other values (961) 24241
98.0%
2024-04-13T22:56:03.994521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22802
13.8%
e 18055
 
10.9%
r 10391
 
6.3%
t 10225
 
6.2%
a 10064
 
6.1%
o 9402
 
5.7%
i 9344
 
5.6%
n 8820
 
5.3%
s 7348
 
4.4%
l 6462
 
3.9%
Other values (44) 52473
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 165386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22802
13.8%
e 18055
 
10.9%
r 10391
 
6.3%
t 10225
 
6.2%
a 10064
 
6.1%
o 9402
 
5.7%
i 9344
 
5.6%
n 8820
 
5.3%
s 7348
 
4.4%
l 6462
 
3.9%
Other values (44) 52473
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 165386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22802
13.8%
e 18055
 
10.9%
r 10391
 
6.3%
t 10225
 
6.2%
a 10064
 
6.1%
o 9402
 
5.7%
i 9344
 
5.6%
n 8820
 
5.3%
s 7348
 
4.4%
l 6462
 
3.9%
Other values (44) 52473
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 165386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22802
13.8%
e 18055
 
10.9%
r 10391
 
6.3%
t 10225
 
6.2%
a 10064
 
6.1%
o 9402
 
5.7%
i 9344
 
5.6%
n 8820
 
5.3%
s 7348
 
4.4%
l 6462
 
3.9%
Other values (44) 52473
31.7%

certificateName
Text

MISSING 

Distinct431
Distinct (%)39.0%
Missing405
Missing (%)26.8%
Memory size11.9 KiB
2024-04-13T22:56:04.436623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.5831826
Min length1

Characters and Unicode

Total characters6175
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)13.1%

Sample

1st rowbox
2nd rowworld
3rd rowradio
4th rowabout
5th rowplant
ValueCountFrequency (%)
effort 10
 
0.9%
nature 9
 
0.8%
before 9
 
0.8%
just 9
 
0.8%
former 8
 
0.7%
fast 8
 
0.7%
require 8
 
0.7%
picture 7
 
0.6%
focus 7
 
0.6%
would 7
 
0.6%
Other values (421) 1024
92.6%
2024-04-13T22:56:05.698092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 861
13.9%
r 503
 
8.1%
a 477
 
7.7%
t 474
 
7.7%
o 446
 
7.2%
s 416
 
6.7%
i 386
 
6.3%
n 369
 
6.0%
l 292
 
4.7%
c 244
 
4.0%
Other values (19) 1707
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 861
13.9%
r 503
 
8.1%
a 477
 
7.7%
t 474
 
7.7%
o 446
 
7.2%
s 416
 
6.7%
i 386
 
6.3%
n 369
 
6.0%
l 292
 
4.7%
c 244
 
4.0%
Other values (19) 1707
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 861
13.9%
r 503
 
8.1%
a 477
 
7.7%
t 474
 
7.7%
o 446
 
7.2%
s 416
 
6.7%
i 386
 
6.3%
n 369
 
6.0%
l 292
 
4.7%
c 244
 
4.0%
Other values (19) 1707
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 861
13.9%
r 503
 
8.1%
a 477
 
7.7%
t 474
 
7.7%
o 446
 
7.2%
s 416
 
6.7%
i 386
 
6.3%
n 369
 
6.0%
l 292
 
4.7%
c 244
 
4.0%
Other values (19) 1707
27.6%
Distinct598
Distinct (%)54.1%
Missing405
Missing (%)26.8%
Memory size11.9 KiB
2024-04-13T22:56:06.065261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length67
Median length49
Mean length35.723327
Min length13

Characters and Unicode

Total characters39510
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique270 ?
Unique (%)24.4%

Sample

1st rowWhom might when weight own.
2nd rowProcess its direction yeah.
3rd rowWhom reach part career.
4th rowAgent network pattern book identify.
5th rowHair often any help treat oil event up.
ValueCountFrequency (%)
book 29
 
0.5%
remember 20
 
0.3%
difference 19
 
0.3%
cold 19
 
0.3%
upon 18
 
0.3%
or 18
 
0.3%
edge 17
 
0.3%
employee 17
 
0.3%
time 16
 
0.3%
fear 16
 
0.3%
Other values (942) 5878
96.9%
2024-04-13T22:56:06.646359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4961
 
12.6%
e 4324
 
10.9%
t 2581
 
6.5%
a 2507
 
6.3%
r 2465
 
6.2%
o 2417
 
6.1%
i 2249
 
5.7%
n 2205
 
5.6%
l 1686
 
4.3%
s 1615
 
4.1%
Other values (42) 12500
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4961
 
12.6%
e 4324
 
10.9%
t 2581
 
6.5%
a 2507
 
6.3%
r 2465
 
6.2%
o 2417
 
6.1%
i 2249
 
5.7%
n 2205
 
5.6%
l 1686
 
4.3%
s 1615
 
4.1%
Other values (42) 12500
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4961
 
12.6%
e 4324
 
10.9%
t 2581
 
6.5%
a 2507
 
6.3%
r 2465
 
6.2%
o 2417
 
6.1%
i 2249
 
5.7%
n 2205
 
5.6%
l 1686
 
4.3%
s 1615
 
4.1%
Other values (42) 12500
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4961
 
12.6%
e 4324
 
10.9%
t 2581
 
6.5%
a 2507
 
6.3%
r 2465
 
6.2%
o 2417
 
6.1%
i 2249
 
5.7%
n 2205
 
5.6%
l 1686
 
4.3%
s 1615
 
4.1%
Other values (42) 12500
31.6%

issuingAuthority
Text

MISSING 

Distinct590
Distinct (%)53.3%
Missing405
Missing (%)26.8%
Memory size11.9 KiB
2024-04-13T22:56:06.994696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length27
Mean length16.28481
Min length6

Characters and Unicode

Total characters18011
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique263 ?
Unique (%)23.8%

Sample

1st rowMitchell Inc
2nd rowAdkins-Fisher
3rd rowKing Inc
4th rowKnight-Garcia
5th rowBrown, Pierce and Blackburn
ValueCountFrequency (%)
and 419
 
16.0%
group 83
 
3.2%
plc 76
 
2.9%
sons 61
 
2.3%
inc 61
 
2.3%
llc 52
 
2.0%
ltd 51
 
1.9%
smith 31
 
1.2%
lopez 23
 
0.9%
brown 23
 
0.9%
Other values (627) 1745
66.5%
2024-04-13T22:56:07.480700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1644
 
9.1%
1519
 
8.4%
a 1470
 
8.2%
e 1267
 
7.0%
o 1199
 
6.7%
r 1184
 
6.6%
s 826
 
4.6%
d 794
 
4.4%
l 792
 
4.4%
i 727
 
4.0%
Other values (43) 6589
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18011
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1644
 
9.1%
1519
 
8.4%
a 1470
 
8.2%
e 1267
 
7.0%
o 1199
 
6.7%
r 1184
 
6.6%
s 826
 
4.6%
d 794
 
4.4%
l 792
 
4.4%
i 727
 
4.0%
Other values (43) 6589
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18011
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1644
 
9.1%
1519
 
8.4%
a 1470
 
8.2%
e 1267
 
7.0%
o 1199
 
6.7%
r 1184
 
6.6%
s 826
 
4.6%
d 794
 
4.4%
l 792
 
4.4%
i 727
 
4.0%
Other values (43) 6589
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18011
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1644
 
9.1%
1519
 
8.4%
a 1470
 
8.2%
e 1267
 
7.0%
o 1199
 
6.7%
r 1184
 
6.6%
s 826
 
4.6%
d 794
 
4.4%
l 792
 
4.4%
i 727
 
4.0%
Other values (43) 6589
36.6%

validityPeriodMonths
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.293183
Minimum0
Maximum48
Zeros405
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2024-04-13T22:56:07.674616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q333
95-th percentile45
Maximum48
Range48
Interquartile range (IQR)33

Descriptive statistics

Standard deviation16.31919
Coefficient of variation (CV)0.89209134
Kurtosis-1.3526952
Mean18.293183
Median Absolute Deviation (MAD)17
Skewness0.30630429
Sum27641
Variance266.31597
MonotonicityNot monotonic
2024-04-13T22:56:07.847191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 405
26.8%
35 47
 
3.1%
28 38
 
2.5%
41 38
 
2.5%
23 36
 
2.4%
45 33
 
2.2%
7 33
 
2.2%
22 33
 
2.2%
39 30
 
2.0%
12 29
 
1.9%
Other values (39) 789
52.2%
ValueCountFrequency (%)
0 405
26.8%
1 22
 
1.5%
2 29
 
1.9%
3 21
 
1.4%
4 20
 
1.3%
5 17
 
1.1%
6 22
 
1.5%
7 33
 
2.2%
8 27
 
1.8%
9 22
 
1.5%
ValueCountFrequency (%)
48 20
1.3%
47 27
1.8%
46 19
1.3%
45 33
2.2%
44 28
1.9%
43 29
1.9%
42 18
1.2%
41 38
2.5%
40 9
 
0.6%
39 30
2.0%

CurentStatus
Categorical

MISSING 

Distinct3
Distinct (%)0.2%
Missing26
Missing (%)1.7%
Memory size11.9 KiB
Rejected
496 
Pending
495 
Approved
494 

Length

Max length8
Median length8
Mean length7.6666667
Min length7

Characters and Unicode

Total characters11385
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowRejected
3rd rowApproved
4th rowApproved
5th rowApproved

Common Values

ValueCountFrequency (%)
Rejected 496
32.8%
Pending 495
32.8%
Approved 494
32.7%
(Missing) 26
 
1.7%

Length

2024-04-13T22:56:08.003712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T22:56:08.146152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rejected 496
33.4%
pending 495
33.3%
approved 494
33.3%

Most occurring characters

ValueCountFrequency (%)
e 2477
21.8%
d 1485
13.0%
n 990
 
8.7%
p 988
 
8.7%
R 496
 
4.4%
j 496
 
4.4%
c 496
 
4.4%
t 496
 
4.4%
P 495
 
4.3%
i 495
 
4.3%
Other values (5) 2471
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2477
21.8%
d 1485
13.0%
n 990
 
8.7%
p 988
 
8.7%
R 496
 
4.4%
j 496
 
4.4%
c 496
 
4.4%
t 496
 
4.4%
P 495
 
4.3%
i 495
 
4.3%
Other values (5) 2471
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2477
21.8%
d 1485
13.0%
n 990
 
8.7%
p 988
 
8.7%
R 496
 
4.4%
j 496
 
4.4%
c 496
 
4.4%
t 496
 
4.4%
P 495
 
4.3%
i 495
 
4.3%
Other values (5) 2471
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2477
21.8%
d 1485
13.0%
n 990
 
8.7%
p 988
 
8.7%
R 496
 
4.4%
j 496
 
4.4%
c 496
 
4.4%
t 496
 
4.4%
P 495
 
4.3%
i 495
 
4.3%
Other values (5) 2471
21.7%

comments
Text

MISSING 

Distinct990
Distinct (%)100.0%
Missing521
Missing (%)34.5%
Memory size11.9 KiB
2024-04-13T22:56:08.570396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length201
Median length159
Mean length145.53535
Min length46

Characters and Unicode

Total characters144080
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique990 ?
Unique (%)100.0%

Sample

1st rowAgent oil away. That among offer tell most. Number plan effort child doctor beyond wonder. Herself change bank happy. Suffer issue foreign candidate despite imagine.
2nd rowAfter information practice response teacher. Identify main doctor skill large. Eat group window watch art hotel prevent.
3rd rowDoor audience month test police. Service stage very entire account resource do. Likely claim each prove office research money.
4th rowPerson opportunity probably local response. Citizen director thought exactly drive education case.
5th rowEnough media hard today theory guess defense same. Employee debate leg yard leave reality. Begin unit free behavior chance voice above.
ValueCountFrequency (%)
world 38
 
0.2%
pay 37
 
0.2%
age 37
 
0.2%
win 35
 
0.2%
our 35
 
0.2%
character 34
 
0.2%
establish 34
 
0.2%
happen 34
 
0.2%
rock 34
 
0.2%
open 34
 
0.2%
Other values (961) 21206
98.4%
2024-04-13T22:56:09.280449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19873
13.8%
e 15634
 
10.9%
t 9184
 
6.4%
r 9057
 
6.3%
a 8769
 
6.1%
o 8400
 
5.8%
i 8144
 
5.7%
n 7591
 
5.3%
s 6292
 
4.4%
l 5722
 
4.0%
Other values (44) 45414
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19873
13.8%
e 15634
 
10.9%
t 9184
 
6.4%
r 9057
 
6.3%
a 8769
 
6.1%
o 8400
 
5.8%
i 8144
 
5.7%
n 7591
 
5.3%
s 6292
 
4.4%
l 5722
 
4.0%
Other values (44) 45414
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19873
13.8%
e 15634
 
10.9%
t 9184
 
6.4%
r 9057
 
6.3%
a 8769
 
6.1%
o 8400
 
5.8%
i 8144
 
5.7%
n 7591
 
5.3%
s 6292
 
4.4%
l 5722
 
4.0%
Other values (44) 45414
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19873
13.8%
e 15634
 
10.9%
t 9184
 
6.4%
r 9057
 
6.3%
a 8769
 
6.1%
o 8400
 
5.8%
i 8144
 
5.7%
n 7591
 
5.3%
s 6292
 
4.4%
l 5722
 
4.0%
Other values (44) 45414
31.5%

rating
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23163468
Minimum-3
Maximum5
Zeros521
Zeros (%)34.5%
Negative519
Negative (%)34.3%
Memory size11.9 KiB
2024-04-13T22:56:09.448033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile-3
Q1-1
median0
Q31
95-th percentile5
Maximum5
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2293835
Coefficient of variation (CV)9.6245672
Kurtosis-0.34538906
Mean0.23163468
Median Absolute Deviation (MAD)1
Skewness0.52641704
Sum350
Variance4.9701509
MonotonicityNot monotonic
2024-04-13T22:56:09.578291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 521
34.5%
-3 213
14.1%
-1 210
13.9%
1 101
 
6.7%
2 100
 
6.6%
4 96
 
6.4%
-2 96
 
6.4%
5 92
 
6.1%
3 82
 
5.4%
ValueCountFrequency (%)
-3 213
14.1%
-2 96
 
6.4%
-1 210
13.9%
0 521
34.5%
1 101
 
6.7%
2 100
 
6.6%
3 82
 
5.4%
4 96
 
6.4%
5 92
 
6.1%
ValueCountFrequency (%)
5 92
 
6.1%
4 96
 
6.4%
3 82
 
5.4%
2 100
 
6.6%
1 101
 
6.7%
0 521
34.5%
-1 210
13.9%
-2 96
 
6.4%
-3 213
14.1%

skillName
Categorical

MISSING 

Distinct17
Distinct (%)1.1%
Missing26
Missing (%)1.7%
Memory size11.9 KiB
Flask
108 
Alteryx
 
99
Python
 
98
Presenting
 
95
AWS
 
90
Other values (12)
995 

Length

Max length12
Median length9
Mean length6.7117845
Min length3

Characters and Unicode

Total characters9967
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDBT
2nd rowReact Native
3rd rowFlask
4th rowReact
5th rowReact Native

Common Values

ValueCountFrequency (%)
Flask 108
 
7.1%
Alteryx 99
 
6.6%
Python 98
 
6.5%
Presenting 95
 
6.3%
AWS 90
 
6.0%
DBT 89
 
5.9%
ADF 88
 
5.8%
Project Mgmt 87
 
5.8%
PHP 86
 
5.7%
Django 84
 
5.6%
Other values (7) 561
37.1%

Length

2024-04-13T22:56:09.728328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aws 174
 
9.6%
react 156
 
8.6%
flask 108
 
6.0%
alteryx 99
 
5.5%
python 98
 
5.4%
presenting 95
 
5.2%
dbt 89
 
4.9%
adf 88
 
4.9%
project 87
 
4.8%
mgmt 87
 
4.8%
Other values (9) 729
40.3%

Most occurring characters

ValueCountFrequency (%)
e 850
 
8.5%
a 763
 
7.7%
t 702
 
7.0%
n 539
 
5.4%
P 526
 
5.3%
l 454
 
4.6%
A 444
 
4.5%
r 438
 
4.4%
o 427
 
4.3%
g 349
 
3.5%
Other values (28) 4475
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 850
 
8.5%
a 763
 
7.7%
t 702
 
7.0%
n 539
 
5.4%
P 526
 
5.3%
l 454
 
4.6%
A 444
 
4.5%
r 438
 
4.4%
o 427
 
4.3%
g 349
 
3.5%
Other values (28) 4475
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 850
 
8.5%
a 763
 
7.7%
t 702
 
7.0%
n 539
 
5.4%
P 526
 
5.3%
l 454
 
4.6%
A 444
 
4.5%
r 438
 
4.4%
o 427
 
4.3%
g 349
 
3.5%
Other values (28) 4475
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 850
 
8.5%
a 763
 
7.7%
t 702
 
7.0%
n 539
 
5.4%
P 526
 
5.3%
l 454
 
4.6%
A 444
 
4.5%
r 438
 
4.4%
o 427
 
4.3%
g 349
 
3.5%
Other values (28) 4475
44.9%

total_project_days
Real number (ℝ)

ZEROS 

Distinct296
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.09596
Minimum0
Maximum364
Zeros400
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2024-04-13T22:56:09.882632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median106
Q3236
95-th percentile333
Maximum364
Range364
Interquartile range (IQR)236

Descriptive statistics

Standard deviation120.59427
Coefficient of variation (CV)0.9341444
Kurtosis-1.2725992
Mean129.09596
Median Absolute Deviation (MAD)106
Skewness0.40817629
Sum195064
Variance14542.978
MonotonicityNot monotonic
2024-04-13T22:56:10.105823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400
 
26.5%
306 14
 
0.9%
333 13
 
0.9%
360 13
 
0.9%
247 12
 
0.8%
219 11
 
0.7%
212 10
 
0.7%
195 10
 
0.7%
21 10
 
0.7%
39 9
 
0.6%
Other values (286) 1009
66.8%
ValueCountFrequency (%)
0 400
26.5%
1 4
 
0.3%
2 8
 
0.5%
3 5
 
0.3%
4 1
 
0.1%
5 9
 
0.6%
6 3
 
0.2%
7 9
 
0.6%
8 8
 
0.5%
9 5
 
0.3%
ValueCountFrequency (%)
364 3
 
0.2%
360 13
0.9%
359 2
 
0.1%
358 2
 
0.1%
357 4
 
0.3%
355 4
 
0.3%
354 3
 
0.2%
352 2
 
0.1%
351 2
 
0.1%
349 7
0.5%

chanceToApprove
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1846459
Minimum0
Maximum12
Zeros405
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2024-04-13T22:56:10.370097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile8
Maximum12
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7586689
Coefficient of variation (CV)0.86624038
Kurtosis0.18299885
Mean3.1846459
Median Absolute Deviation (MAD)2
Skewness0.69363389
Sum4812
Variance7.6102542
MonotonicityNot monotonic
2024-04-13T22:56:10.544821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 405
26.8%
4 212
14.0%
3 195
12.9%
2 174
11.5%
5 155
 
10.3%
6 102
 
6.8%
8 80
 
5.3%
7 77
 
5.1%
1 77
 
5.1%
12 24
 
1.6%
ValueCountFrequency (%)
0 405
26.8%
1 77
 
5.1%
2 174
11.5%
3 195
12.9%
4 212
14.0%
5 155
 
10.3%
6 102
 
6.8%
7 77
 
5.1%
8 80
 
5.3%
10 10
 
0.7%
ValueCountFrequency (%)
12 24
 
1.6%
10 10
 
0.7%
8 80
 
5.3%
7 77
 
5.1%
6 102
6.8%
5 155
10.3%
4 212
14.0%
3 195
12.9%
2 174
11.5%
1 77
 
5.1%
Distinct502
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2024-04-13T22:56:11.049886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length21
Median length18
Mean length13.031105
Min length8

Characters and Unicode

Total characters19690
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)7.0%

Sample

1st rowAdmin Admin
2nd rowAdmin Admin
3rd rowHARSH SINGH
4th rowHARSH SINGH
5th rowHARSH SINGH
ValueCountFrequency (%)
brown 35
 
1.2%
john 34
 
1.1%
robert 31
 
1.0%
smith 30
 
1.0%
david 28
 
0.9%
michael 28
 
0.9%
jennifer 26
 
0.9%
kelly 25
 
0.8%
moore 25
 
0.8%
christopher 25
 
0.8%
Other values (544) 2735
90.5%
2024-04-13T22:56:11.638834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1820
 
9.2%
a 1770
 
9.0%
1511
 
7.7%
n 1447
 
7.3%
r 1431
 
7.3%
i 1179
 
6.0%
o 1167
 
5.9%
l 1002
 
5.1%
s 850
 
4.3%
t 689
 
3.5%
Other values (41) 6824
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1820
 
9.2%
a 1770
 
9.0%
1511
 
7.7%
n 1447
 
7.3%
r 1431
 
7.3%
i 1179
 
6.0%
o 1167
 
5.9%
l 1002
 
5.1%
s 850
 
4.3%
t 689
 
3.5%
Other values (41) 6824
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1820
 
9.2%
a 1770
 
9.0%
1511
 
7.7%
n 1447
 
7.3%
r 1431
 
7.3%
i 1179
 
6.0%
o 1167
 
5.9%
l 1002
 
5.1%
s 850
 
4.3%
t 689
 
3.5%
Other values (41) 6824
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1820
 
9.2%
a 1770
 
9.0%
1511
 
7.7%
n 1447
 
7.3%
r 1431
 
7.3%
i 1179
 
6.0%
o 1167
 
5.9%
l 1002
 
5.1%
s 850
 
4.3%
t 689
 
3.5%
Other values (41) 6824
34.7%

Interactions

2024-04-13T22:55:58.344511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:54.975500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:55.766409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:56.737032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:57.538874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:58.488422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:55.188859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:56.094936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:56.885979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:57.689366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:58.643357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:55.323848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:56.237778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:57.033897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:57.844434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:58.804203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:55.472154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:56.390992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:57.201477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:58.037700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:58.956988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:55.626966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:56.575328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:57.380884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-13T22:55:58.189287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-13T22:55:59.185292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T22:55:59.595101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

userId_xproficiencyVerificationStatushackerRankScoredesignationprojectNameProjectDescriptioncertificateNameCertificateDescriptionissuingAuthorityvalidityPeriodMonthsCurentStatuscommentsratingskillNametotal_project_dayschanceToApprovefullname
0NaNintermediateVerified12.0ArchitectNaNNaNNaNNaNNaN0.0ApprovedAgent oil away. That among offer tell most.\r\nNumber plan effort child doctor beyond wonder. Herself change bank happy. Suffer issue foreign candidate despite imagine.5.0DBT0.00.0Admin Admin
166129473addf941b98f24c4aadvancedVerified71.0ArchitectNaNNaNboxWhom might when weight own.Mitchell Inc28.0RejectedAfter information practice response teacher.\r\nIdentify main doctor skill large. Eat group window watch art hotel prevent.4.0React Native0.04.0Admin Admin
266129472addf941b98f24c39advancedVerified6.0Principal ArchitectHicks-RodriguezName history consumer allow expert. Because enjoy establish perform recognize teacher.\r\nSocial letter food over perhaps appear. Two walk least generation small admit.\r\nThought different else week here.worldProcess its direction yeah.Adkins-Fisher37.0ApprovedDoor audience month test police. Service stage very entire account resource do. Likely claim each prove office research money.1.0Flask25.04.0HARSH SINGH
366129484addf941b98f24d52advancedVerified54.0Principal ArchitectNaNNaNradioWhom reach part career.King Inc47.0ApprovedPerson opportunity probably local response. Citizen director thought exactly drive education case.2.0React0.05.0HARSH SINGH
4NaNadvancedVerified81.0Principal ArchitectNaNNaNNaNNaNNaN0.0ApprovedEnough media hard today theory guess defense same. Employee debate leg yard leave reality. Begin unit free behavior chance voice above.4.0React Native0.00.0HARSH SINGH
5NaNNaNNaN0.0Sr. Software EngineerNaNNaNNaNNaNNaN0.0NaNNaN0.0NaN0.00.0Rajib Parbat
6NaNintermediateVerified3.0Solution EnablerKeller PLCMachine network dream here ask detail single. Paper improve third laugh entire cause despite. He study others.\r\nThroughout executive old travel. Per health news. Put population us receive.NaNNaNNaN0.0RejectedLoss quite small court left oil leader herself. Brother practice system professional home office than. Catch two coach country prevent financial light.2.0Project Mgmt305.00.0swarnadeep pramanik
76612946caddf941b98f24be3intermediateNot Verified60.0Solution EnablerSchultz, Jacobs and WarnerRelationship employee wife. Design for hour office factor eight hear.\r\nNumber tree school church capital. Mission daughter student may form summer. Consider impact heart.aboutAgent network pattern book identify.Knight-Garcia3.0PendingNaN0.0Presenting304.012.0mirror acctwo
8NaNadvancedNot Verified19.0Solution EnablerCampos and SonsInformation begin article true. Base body clearly hotel artist.\r\nFinish general different director. Public strong along business this former.NaNNaNNaN0.0RejectedCommunity life lawyer technology big I. Response go machine history amount process child nothing.-1.0Project Mgmt303.00.0mirror acctwo
96612947daddf941b98f24cf0intermediateNot Verified13.0Solution EnablerNaNNaNplantHair often any help treat oil event up.Brown, Pierce and Blackburn42.0ApprovedWear power reduce movement. Resource team president good.\r\nMeasure generation phone. Arrive morning campaign suddenly tend forward someone. Site trouble million thousand hear.-1.0AWS Lambda0.02.0mirror acctwo
userId_xproficiencyVerificationStatushackerRankScoredesignationprojectNameProjectDescriptioncertificateNameCertificateDescriptionissuingAuthorityvalidityPeriodMonthsCurentStatuscommentsratingskillNametotal_project_dayschanceToApprovefullname
15016612946baddf941b98f24bd1beginnerNot Verified13.0Software EngineerWolf-WhiteLike about see practice low hit my truth. Top continue green in.\r\nFree skin agency. Sign west while culture continue must.electionCoach call age recognize public.Newton PLC41.0RejectedMember issue throughout make various. Dinner today kind give tree daughter fund.\r\nEat at simply soldier scientist imagine. Development family more factor various.-3.0Presenting331.03.0Lori Aguilar
1502NaNintermediateVerified57.0Software EngineerFields, Scott and AllenTreatment financial a head resource. Shoulder Mrs before listen offer win alone. Election message talk move similar inside. Leg day door include much.NaNNaNNaN0.0PendingNaN0.0Snowflake305.00.0Lori Aguilar
1503NaNintermediateVerified43.0Sr. Software EngineerBrowning, Faulkner and LeeBox test blood go most. Whatever yourself well peace. Character floor grow never before call next.\r\nCompany admit near. Order station such that like.\r\nTrade best citizen avoid. Might democratic friend.NaNNaNNaN0.0ApprovedEasy all alone on respond. Population inside local fall own prevent message. Order long series growth professional director.3.0AWS315.00.0Ralph Beltran
150466129478addf941b98f24c97advancedVerified51.0Sr. Software EngineerKlein-GoodmanIndicate community no. Public writer data professor indicate radio side.\r\nDay my mention imagine including southern.\r\nSign exactly bad own live. Alone forward safe reduce. Tv stuff hospital kid mind.dinnerStop Mrs capital early.Smith PLC48.0PendingNaN0.0Power BI18.06.0Ralph Beltran
150566129484addf941b98f24d5cbeginnerVerified23.0Solution EnablerNaNNaNprocessPolitical direction information PM subject.King-Green45.0PendingNaN0.0Snowflake0.01.0Melissa Jones
1506NaNintermediateNot Verified61.0Solution EnablerVega-YangSeek hold view. Season station purpose until special cultural no. Continue manager kind positive house themselves company since.NaNNaNNaN0.0PendingNaN0.0Flask23.00.0Melissa Jones
150766129472addf941b98f24c3fadvancedNot Verified67.0Sr. Software EngineerLittle-EdwardsMovie state manager a close so participant. Tax once play far.\r\nAdult heavy first relate baby want skin. Perhaps half health receive level put improve.requireSense agree purpose American rate time international.Smith, Logan and Morris28.0RejectedLittle office summer her thing father low design. Western green material guess necessary exactly event. Of end democratic admit.\r\nCard character remain. Chance let rate similar remain yard.-3.0Flask18.08.0Glenda Gordon
150866129486addf941b98f24d74intermediateVerified47.0Sr. Software EngineerNaNNaNwouldPlayer have rise feeling should budget.Carney, Gray and Foley26.0ApprovedStand worker including bring. Finish or language official owner cut notice.\r\nWestern late budget morning. Card value other you deal drop. South visit final reality learn cut high.3.0Presenting0.04.0Glenda Gordon
150966129488addf941b98f24d90beginnerNot Verified54.0Sr. Software EngineerJames GroupHimself maintain kind part activity. Might whose seek.\r\nSerious school ball night attack sea job various. Those marriage service child final window.issueSon executive capital probably.Willis PLC25.0RejectedMarriage reveal develop computer difference over film scientist. Nor win whom authority. Mother production or employee usually.\r\nBuy coach key so itself. Notice society step.-3.0Snowflake74.02.0Glenda Gordon
1510NaNbeginnerVerified27.0Sr. Software EngineerNaNNaNNaNNaNNaN0.0PendingNaN0.0AWS0.00.0Glenda Gordon